





import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
			import torch

			from transformers.generation import (
			    MaxLengthCriteria,
			    MaxNewTokensCriteria,
			    MaxTimeCriteria,
			    StoppingCriteriaList,
			    validate_stopping_criteria,
			)
@require_torch
class   a_ (			unittest.TestCase      ):

			def      lowerCAmelCase(   self   :  int     , UpperCAmelCase__   :  Tuple						):






							"""simple docstring"""
							snake_case   :					int    						=				3
							snake_case   :					Dict    						=				250

							snake_case   :					List[Any]    						=				ids_tensor((batch_size, length)     , UpperCAmelCase__						)
							snake_case   :					List[str]    						=				torch.ones((batch_size, length)     , device=UpperCAmelCase__     , dtype=torch.float						) / length
							return input_ids, scores

			def      lowerCAmelCase(   self   :  str						):






							"""simple docstring"""
							snake_case						,						snake_case   :					Tuple    						=				self._get_tensors(5						)

							snake_case   :					List[Any]    						=				StoppingCriteriaList(
							    [
							        MaxLengthCriteria(max_length=10						),
							        MaxTimeCriteria(max_time=0.1						),
							    ]						)

							self.assertFalse(criteria(UpperCAmelCase__     , UpperCAmelCase__						)						)

							snake_case						,						snake_case   :					Optional[int]    						=				self._get_tensors(9						)
							self.assertFalse(criteria(UpperCAmelCase__     , UpperCAmelCase__						)						)

							snake_case						,						snake_case   :					int    						=				self._get_tensors(10						)
							self.assertTrue(criteria(UpperCAmelCase__     , UpperCAmelCase__						)						)

			def      lowerCAmelCase(   self   :  Any						):






							"""simple docstring"""
							snake_case   :					Optional[Any]    						=				MaxLengthCriteria(max_length=10						)

							snake_case						,						snake_case   :					Optional[int]    						=				self._get_tensors(5						)
							self.assertFalse(criteria(UpperCAmelCase__     , UpperCAmelCase__						)						)

							snake_case						,						snake_case   :					Union[str, Any]    						=				self._get_tensors(9						)
							self.assertFalse(criteria(UpperCAmelCase__     , UpperCAmelCase__						)						)

							snake_case						,						snake_case   :					Dict    						=				self._get_tensors(10						)
							self.assertTrue(criteria(UpperCAmelCase__     , UpperCAmelCase__						)						)

			def      lowerCAmelCase(   self   :  Dict						):






							"""simple docstring"""
							snake_case   :					Optional[int]    						=				MaxNewTokensCriteria(start_length=5     , max_new_tokens=5						)

							snake_case						,						snake_case   :					int    						=				self._get_tensors(5						)
							self.assertFalse(criteria(UpperCAmelCase__     , UpperCAmelCase__						)						)

							snake_case						,						snake_case   :					int    						=				self._get_tensors(9						)
							self.assertFalse(criteria(UpperCAmelCase__     , UpperCAmelCase__						)						)

							snake_case						,						snake_case   :					Any    						=				self._get_tensors(10						)
							self.assertTrue(criteria(UpperCAmelCase__     , UpperCAmelCase__						)						)

							snake_case   :					Optional[int]    						=				StoppingCriteriaList([criteria]						)
							self.assertEqual(criteria_list.max_length     , 10						)

			def      lowerCAmelCase(   self   :  Optional[int]						):






							"""simple docstring"""
							snake_case						,						snake_case   :					int    						=				self._get_tensors(5						)

							snake_case   :					Optional[Any]    						=				MaxTimeCriteria(max_time=0.1						)
							self.assertFalse(criteria(UpperCAmelCase__     , UpperCAmelCase__						)						)

							snake_case   :					Tuple    						=				MaxTimeCriteria(max_time=0.1     , initial_timestamp=time.time() - 0.2						)
							self.assertTrue(criteria(UpperCAmelCase__     , UpperCAmelCase__						)						)



			def      lowerCAmelCase(   self   :  Optional[int]						):






							"""simple docstring"""
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10						)]						)     , 10						)

							with self.assertWarns(UpperCAmelCase__						):
											validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10						)]						)     , 11						)

							snake_case   :					List[Any]    						=				validate_stopping_criteria(StoppingCriteriaList()     , 11						)

							self.assertEqual(len(UpperCAmelCase__						)     , 1						)







"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'fnet'





      def __init__(			self ,		lowercase=32_000 ,		lowercase=768 ,		lowercase=12 ,		lowercase=3_072 ,		lowercase="gelu_new" ,		lowercase=0.1 ,		lowercase=512 ,		lowercase=4 ,		lowercase=0.02 ,		lowercase=1e-12 ,		lowercase=False ,		lowercase=512 ,		lowercase=3 ,		lowercase=1 ,		lowercase=2 ,		**lowercase ,		)						->	int:
            super().__init__(pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		**lowercase					)

            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			max_position_embeddings
            lowerCAmelCase    		=			hidden_size
            lowerCAmelCase    		=			num_hidden_layers
            lowerCAmelCase    		=			intermediate_size
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			hidden_dropout_prob
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			type_vocab_size
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			use_tpu_fourier_optimizations
            lowerCAmelCase    		=			tpu_short_seq_length






